AI for Business Leaders NanoDegree earned by MoniGarr

MoniGarr earned the AI for Business Leaders Nano Degree on February 8th, 2022 from the Udacity Nano Degree program.


  • AI for Business Leaders Executive Program
    • The Paradigm Shift
    • The Math Behind the Magic
    • Architectures of AI Systems
    • Working with Data
    • Accuracy, Bias and Ethics
    • Gathering Feedback
    • Thinking Bigger
    • Delivering an ML / AI Strategy: Capstone Project (Architectures for 3 Use Cases). Request Access to View MoniGarr’s work resulted in MoniGarr recommending 1. Lexical Normalization. 2. Part of Speech Tagging. 3. Grammatical Error Correction.


Reviewer Note

Congratulations, you’ve passed this project 🏆!

Great work!

🔰 SPECIAL CALL OUT TO YOU: You did well under information overload – hopefully it will be a rewarding experience for you. I have shared a few sites for reference. Loved the manner on how you’re able to weave the information, and transform them into insights. Keep upskilling for your future work reference – see some sites below, if you can read through every now and then, you’re at the edge of AI research / progress.

You’re already up there, and ready to advanced further. I’d encourage you to continue reading / dig deeper into AI / ML strategies and data science. Here’s a few good sites for you:…

And one last thing, I’d like to leave this rather interesting way of shaping AI & ML project, hope this will take you forward further…



Paradigm Shifts

Success Criteria Specifications

Define a before and after vision of a business process, achievable only through ML/AI.

Each grid of storyboard includes a setup, transformation, resolution, and result. Data are clearly identified, though perhaps not yet explained, in the setup step. Each result is a logical conclusion, given the transformation and resolution provided.

Synthesize, expand on, and recreate the AI/ML implementations of others by drawing analogies to use cases in other domains.

Articles/papers linked to provide intuitive connections to the storyboards. The needs for ML/AI and “shift” in paradigm are cleanly articulated. At least one risk is provided for each use case.

Articulate how different ML/AI capabilities may be applied in a variety of settings.

Use cases provided show depth of thought into ML/AI’s differentiation. There is breadth in application (e.g., optimization vs. prediction, or NLP vs. computer vision) of various techniques.

Mathematical Constraints as a First Filter

Success Criteria Specifications

Determine the potential of using ML/AI approaches by analyzing relevant data characteristics.

Justifications for data rankings in the Five V questions are well reasoned and explained.

Determine the types of AI/ML capabilities needed for a given problem, based on available data, and not vice-versa.

Approaches (e.g. classification, regression) are correctly selected based on the storyboards provided. Explanations correctly incorporate key terms such as “class”, “continuous”, “binary”, “objective”, “policy”, “state”, “reward.” Applications are appropriate and not forced.

Articulate sound judgments around feasibility vs. impact based on the data characteristics of a given use case.

“Grid” reflects discretion within the use cases provided and shows understanding of relative strengths. Strengths along each axis roughly map to ratings provided in prior exercise and are appropriate for the use case context.

Ideating on Architectures with Common Patterns

Success Criteria Specifications

Correctly identify the ML/AI capabilities needed to deliver a contemplated use case.

Machine learning capabilities align with the apparent objective. Non-ML capabilities are likewise used when appropriate. Annotations are used where connection isn’t apparent.

Show how data inputs and capabilities flow as inputs and outputs in a basic and intuitive fashion that is digestible to both business and technical stakeholders.

All capabilities have some form of input/output visualization. Different layers are correctly called out. Training data ties back to Project Step 2 discussion.

Demonstrate how technology and data flows facilitate the user experience by connecting “capabilities” back to actual “experiences” and correctly identifying “end users” as opposed to “stakeholders.”

The submission identifies the end user. The user experience layer is present. Output flows suggest how a human will be involved in the process and make (or perhaps simply observe) a final decision.

Hard Choices Around Data

Success Criteria Specifications

Assess trade-offs and prioritize or reprioritize a series of ML/AI use cases accordingly.

Student has completed all requirements for this step as directed. Final grid/board reflects trade-off assessments accurately.

Operational Considerations – Accuracy, Bias, and Ethics

Success Criteria Specifications

Assess model effectiveness in machine-learning use cases. Develop longitudinal plans to measure the success of models.

Model effectiveness is clearly defined relative to some form of human or current-state benchmark. Model effectiveness and probability/confidence are measured separately, and not conflated. Model effectiveness is treated as an ongoing measure, and the notion of drift or loss of effectiveness is accounted for.

Explain model bias and overfitting as these concepts pertain to real-world scenarios . Suggest ways to prevent these scenarios on an ongoing basis.

Student’s explanation clearly demonstrates an understanding of the difference between real-world bias vs. model bias. Student’s explanation discusses both bias and overfitting, and highlights risks of either particular to this scenario. Bias and overfitting are both discussed as ongoing, longitudinal concerns. A monitoring/mitigation plan is present.

Address ethical concerns of AI/ML models in their work. Present approaches to mitigate these risks over time.

Ethical concerns are assessed as often related to, but not the same as, model effectiveness, bias, or overfitting concerns. Student clearly highlights who potentially impacted parties may be, and highlights all stakeholders — users, users’ customers, their own employees, etc. — who may be affected. Ethics is addressed as an ongoing, longitudinal concern and a process for monitoring and mitigation over time is presented.

Seeking Input from Others

Success Criteria Specifications

Identify and solicit feedback from relevant stakeholders who may have little experience with machine learning or AI, but may be in a better position to assess the business impact of a use case.

At least five filled-out Google forms are submitted, one for each of the email-based, phone, or in-person interviews. Surveys, interviews, etc. cover at least three different use cases being discussed.

Synthesize and analyze feedback that may come from a less informed point of view regarding AI/ML.

Some form of graphical or otherwise intuitive representation of results is provided. Representation provides some form of indicative trend or conclusion that is apparent to the reviewer. Student draws clear, data-driven conclusions that are consistent with the analyses presented.

Prioritize use cases, based on external feedback.

Student emphasizes (implicitly or directly) the differences between feasibility and impact. Student appropriately “weights” feedback based on interviewees’ experiences.

Tying it All Together

Success Criteria Specifications

Tell a cohesive narrative around how ML/AI affects a business, balancing business impact and feasibility.

Student demonstrates holistic knowledge of ML/AI throughout presentation. Wording and storyline of presentation is clear to reviewer. “Big takeaways” that go beyond one exercise or another are present.

Demonstrate full comprehension of prior exercises and why they can be helpful in evaluating an ML/AI business strategy.

Student should draw upon earlier work and learnings, possibly including prioritization grid, architectures, survey results, or any other steps. The use of these exercises should demonstrate a deeper understanding of their purpose in determining a cohesive strategy. Presentation should highlight (implicitly perhaps) how exercises helped to create the priority among use cases.

Suggestions to Make Your Project Stand Out

  • Working on use cases that require multiple types or modes of machine learning without creating a significantly harder technical challenge often shows a deeper appreciation for what a real-world scenario might require.
  • Students with greater technical ability are welcome to provide prototypes, code, or even pseudo-code that demonstrates a deeper understanding or further progress on certain use cases.
  • Compelling storylines and excellent presentations will be well received! Highlight your users’ pain, explain how that pain causes business problems, and how ML/AI can help. A thoughtful, concise submission that has “teeth” is more likely to succeed on the first try than one that is hastily thrown together.